Signals & systems (2nd ed.)
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Design for Manufacturability and Statistical Design: A Comprehensive Approach
Design for Manufacturability and Statistical Design: A Comprehensive Approach
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Proceedings of the 2009 International Conference on Computer-Aided Design
Proceedings of the 47th Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the International Conference on Computer-Aided Design
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Handling discontinuous effects in modeling spatial correlation of wafer-level analog/RF tests
Proceedings of the Conference on Design, Automation and Test in Europe
Automatic clustering of wafer spatial signatures
Proceedings of the 50th Annual Design Automation Conference
Hi-index | 0.00 |
In this paper, we propose a new technique to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that spatially correlated variation carries a unique sparse signature in frequency domain. Based upon this observation, an efficient sparse regression algorithm is applied to accurately separate spatially correlated variation from uncorrelated random variation. An important contribution of this paper is to develop a fast numerical algorithm that reduces the computational time of sparse regression by several orders of magnitude over the traditional implementation. Our experimental results based on silicon measurement data demonstrate that the proposed sparse regression technique can capture spatially correlated variation patterns with high accuracy. The estimation error is reduced by more than 3.5x compared to other traditional methods.